Expert systems in insurance: a review and analysis
International Journal of Intelligent Systems in Accounting and Finance Management - Insurance
An algorithm for suffix stripping
Readings in information retrieval
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Experiments in dynamic critiquing
Proceedings of the 10th international conference on Intelligent user interfaces
Explanation in Recommender Systems
Artificial Intelligence Review
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Trust-inspiring explanation interfaces for recommender systems
Knowledge-Based Systems
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Effective explanations of recommendations: user-centered design
Proceedings of the 2007 ACM conference on Recommender systems
Proceedings of the 2007 international ACM conference on Supporting group work
The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
The value of personalised recommender systems to e-business: a case study
Proceedings of the 2008 ACM conference on Recommender systems
Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
A personalized system for conversational recommendations
Journal of Artificial Intelligence Research
A case study on the effectiveness of recommendations in the mobile internet
Proceedings of the third ACM conference on Recommender systems
MoviExplain: a recommender system with explanations
Proceedings of the third ACM conference on Recommender systems
Tag expression: tagging with feeling
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Recommender Systems: An Introduction
Recommender Systems: An Introduction
Persuasive online-selling in quality and taste domains
EC-Web'06 Proceedings of the 7th international conference on E-Commerce and Web Technologies
Evaluating the effectiveness of explanations for recommender systems
User Modeling and User-Adapted Interaction
The influence of knowledgeable explanations on users' perception of a recommender system
Proceedings of the sixth ACM conference on Recommender systems
Improving recommendation accuracy based on item-specific tag preferences
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
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Recommender systems help users locate possible items of interest more quickly by filtering and ranking them in a personalized way. Some of these systems provide the end user not only with such a personalized item list but also with an explanation which describes why a specific item is recommended and why the system supposes that the user will like it. Besides helping the user understand the output and rationale of the system, the provision of such explanations can also improve the general acceptance, perceived quality, or effectiveness of the system. In recent years, the question of how to automatically generate and present system-side explanations has attracted increased interest in research. Today some basic explanation facilities are already incorporated in e-commerce Web sites such as Amazon.com. In this work, we continue this line of recent research and address the question of how explanations can be communicated to the user in a more effective way. In particular, we present the results of a user study in which users of a recommender system were provided with different types of explanation. We experimented with 10 different explanation types and measured their effects in different dimensions. The explanation types used in the study include both known visualizations from the literature as well as two novel interfaces based on tag clouds. Our study reveals that the content-based tag cloud explanations are particularly helpful to increase the user-perceived level of transparency and to increase user satisfaction even though they demand higher cognitive effort from the user. Based on these insights and observations, we derive a set of possible guidelines for designing or selecting suitable explanations for recommender systems.